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Reseach Article

Removing Noise Dots Points from the Scanning various Images using Epipolar Geometry with Matlab

Published on February 2013 by Abdul Jabbar Shaikh Azad, Patil Yogesh Uttam, Sharad D. Patil, Ramesh R. Manza
International Conference on Recent Trends in Information Technology and Computer Science 2012
Foundation of Computer Science USA
ICRTITCS2012 - Number 6
February 2013
Authors: Abdul Jabbar Shaikh Azad, Patil Yogesh Uttam, Sharad D. Patil, Ramesh R. Manza
c308e4c0-7696-48be-8ee2-a4c91ea42ecb

Abdul Jabbar Shaikh Azad, Patil Yogesh Uttam, Sharad D. Patil, Ramesh R. Manza . Removing Noise Dots Points from the Scanning various Images using Epipolar Geometry with Matlab. International Conference on Recent Trends in Information Technology and Computer Science 2012. ICRTITCS2012, 6 (February 2013), 16-19.

@article{
author = { Abdul Jabbar Shaikh Azad, Patil Yogesh Uttam, Sharad D. Patil, Ramesh R. Manza },
title = { Removing Noise Dots Points from the Scanning various Images using Epipolar Geometry with Matlab },
journal = { International Conference on Recent Trends in Information Technology and Computer Science 2012 },
issue_date = { February 2013 },
volume = { ICRTITCS2012 },
number = { 6 },
month = { February },
year = { 2013 },
issn = 0975-8887,
pages = { 16-19 },
numpages = 4,
url = { /proceedings/icrtitcs2012/number6/10286-1390/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Recent Trends in Information Technology and Computer Science 2012
%A Abdul Jabbar Shaikh Azad
%A Patil Yogesh Uttam
%A Sharad D. Patil
%A Ramesh R. Manza
%T Removing Noise Dots Points from the Scanning various Images using Epipolar Geometry with Matlab
%J International Conference on Recent Trends in Information Technology and Computer Science 2012
%@ 0975-8887
%V ICRTITCS2012
%N 6
%P 16-19
%D 2013
%I International Journal of Computer Applications
Abstract

In this paper, we explore possibilities to improve quality images by using Epipolar geometry with mat lab. The source images can give best results with the best clarity pixel to pixel achievement. The goal is make character segmentation results more reduce the need image for user interaction with the help of Epipolar geometry. We clean up isolated noise dots without removing small dots that are parts of characters. The system has been tested with real camera images or satellite images under various character conditions. We discover a set of high geometric and appearance constraints with low level the images matches' reliable matching results. We use lines because they have some advantages with respect to points, particularly in manmade environments. We cleaned dots on images estimating thresholding simultaneously with transformation method. We are closely trying to solve Mat lab scripts to clean up scanned pages from old manuscripts. To find particular character with clean noise dots from the image database sources.

References
  1. 1. Shaikh A. J. , Kurehsi Anis. M. , & Manza R. R. "Using Epipolar Geometry Applying the Bit Plane On Images Using Matlab" Advances in Computational Research, ISSN: 0975-3273 & E-ISSN: 0975-9085, Volume 4, Issue 1, 2012, pp. -34-37
  2. Rechard Hartly. In defence of the 8-point algorithm. In Proceeding of the 5th international Conference on Computer Vision, Pages 1062-1070,1995.
  3. G. Qiu and S. Sudirman, "Color Image Coding, Indexing and Retrieval Using Binary Space Partitioning Tree,"Proc. IEEE Int'l Conf. Image Processing,Oct. 2001.
  4. H. R. Rabiee, R. L. Kashyap, and H. Radha, "Multiresolution Image Compression with BSP Trees and Multi-Level Block Truncation Coding,"Proc. IEEE Second Int'l Conf. Image Processing,pp. 600-603, 1995.
  5. H. Radha, M. Vetterli, and R. Leonardi, "Image CompressionUsing Binary Space Partitioning Trees,"IEEE Trans. Image Processing,vol. 5, no. 12, pp. 1610-1624, Dec. 1996.
  6. Aggarwal, J. andNandhakumar, N. : 1988, On the compu-tation of motion from sequences of images — a review,Proc. IEEE76(8), 917–935.
  7. J. Gluckman and S. K. Nayar. "Rectifying transformations that minimize resampling effects", In: Proceedings of the
  8. International Conference on Computer Vision and Pattern Recognition, Los Angeles, USA, 2001, 1, pp. 111-117.
  9. A. Fusiello, E. Trucco, and A. Verri. "A compact algorithm for rectification of stereo pairs", Machine Vision and Applications, 2000, 12(1), pp. 16-22.
  10. R. Hartley, "Theory and practice of projective rectification", InternationalJournal of Computer Vision, 1999, 35(2), pp. 115-127.
  11. Francesco Isgro, Trucco, "Projective rectification without epipolar geometry", In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Fort Collins, Colorado, USA, 1999, 1, pp. 94-99.
  12. J. M. LaBerge et al. , Portal Hypertension: Options for Diag-nosis and Treatment, Soc. Cardiovascular and Intervention-al Radiology, 1995.
  13. 2. S. Aylward and E. Bullitt, "Initialization, Noise, Singulari-ties and Scale in Height Ridge Traversal for Tubular Object Centerline Extraction," IEEE Trans. Medical Imaging, vol. 21, no. 2, 2002, pp. 61-75.
  14. 3. E. Bullitt et al. , "Symbolic Description of Intracerebral Ves-sels Segmented from MRA and Evaluation by Comparison with X-Ray Angiograms," Medical Image Analysis, vol. 5,no. 2, 2001, pp. 157-169.
  15. M. Blanco, H. M. Jonathan, and T. A. Dingus. "Evaluating New Technolo-gies to Enhance Night Vision by Looking at Detection and Recognition Distances of Non-Motorists and Objects," in Proc. Human Factors and Ergonomics Society, Minneapolis, MN, Jan. 2001, vol. 5, pp. 1612-1616.
  16. O. Tsimhoni, J. B ¨ argman, T. Minoda, and M. J. Flannagan. "Pedestrian Detection with Near and Far Infrared Night Vision Enhancement," Tech. rep. , The University of Michigan, 2004.
  17. L. Tao, H. Ngo, M. Zhang, A. Livingston, and V. Asari. "A Multi-sensor Image Fusion and Enhancement System for Assisting Drivers in Poor Lighting Conditions," in Proc. IEEE Conf. Applied Imagery and Pattern Recognition Workshop, Washington, DC, Dec. 2005, pp. 106-113.
  18. H. Ngo, L. Tao, M. Zhang, A. Livingston, and V. Asari. "A Visibility Improvement System for Low Vision Drivers by Nonlinear Enhancement of Fused Visible and Infrared Video," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, San Diego, CA, Jun. 2005, pp. 25.
  19. G. Chesi, K. Hashimoto, D. Prattichizzo, and A. Vicino. Keeping features in the field of view in eye-in-hand visual servoing: a switching approach. Robotics, IEEE Transactions on, 20(5):908 –914, October 2004.
  20. F. Conticelli, D. Prattichizzo, A. Bicchi, and F. Guidi. Vision-based dynamic estimation and set-point stabilization of nonholonomic vehicles. InProc. IEEE Int. Conf. on Robotics and Automation, pages 2771–2776, San Francisco, CA, USA,, 2000.
  21. Sal D'Agostino, Commercial machine vision system for traffic monitoring and control, SPIE vol. 1615 (1991) 180-186
  22. Takatoo, M. , et al, Traffic flow measuring system using image processing, SPIEvol. 1197 (1989) 172-180
Index Terms

Computer Science
Information Sciences

Keywords

Epipolar Geometry Fundamental Matrix Thresholding Adaptive Median Filtering